Updated: 2020-07-27 13:38:27 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Contained and Uncontained Disease

A key indicator of mitigation is capping infection. Uncontained disease growth threatens epidemic conditions.

This visualization shows places where current disease levels are below their peak levels (’contained“) and where current disease levels are at an all time high (”uncontained").

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from log_2(R_e) > 0 to log_2(R_e) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

state R_e cases daily_cases
Mississippi 1.27 52294 1449
North Dakota 1.27 5794 136
Montana 1.26 3364 144
Missouri 1.25 38272 1108
Wyoming 1.25 2489 54
Kentucky 1.23 27951 708
Oklahoma 1.23 31113 937
Tennessee 1.18 91859 2530
Arkansas 1.17 37542 854
Indiana 1.17 63855 913
Virginia 1.17 68174 906
West Virginia 1.17 6022 155
Alabama 1.16 80765 2221
California 1.16 464941 11253
Colorado 1.16 44217 603
Nevada 1.16 43723 1306
Nebraska 1.15 24461 267
New Mexico 1.15 19183 341
Wisconsin 1.15 49209 1037
Connecticut 1.14 48526 111
Georgia 1.14 153002 3729
Idaho 1.14 18883 644
Illinois 1.14 171652 1415
Maryland 1.14 84082 840
Florida 1.13 432390 13287
Louisiana 1.13 108293 2468
Minnesota 1.13 50832 717
New Hampshire 1.12 6433 30
Ohio 1.10 84876 1529
Oregon 1.10 16965 373
Texas 1.10 408366 11135
Washington 1.10 55146 984
New Jersey 1.09 180447 306
North Carolina 1.09 113964 2185
South Dakota 1.09 8180 65
Kansas 1.08 26090 514
Maine 1.08 3808 20
Pennsylvania 1.08 112073 945
South Carolina 1.08 82903 2034
Delaware 1.07 14118 108
Michigan 1.07 86692 743
Utah 1.07 38385 677
Iowa 1.06 42670 573
Massachusetts 1.06 115222 296
Vermont 1.04 1404 8
Arizona 1.02 163681 3018
New York 1.02 416617 732
Rhode Island 1.00 16723 73

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. They’re plotted against linear scales. While this hides some important details, the plots are more intuitively interpretable for most people.

## Warning: Removed 1 row(s) containing missing values (geom_path).

Mortality Trend

National Reproduction Rates \(R_e\)

There is also large variation in the distribution of \(R_e\) values. This shows how that distribution has changed over the last three weeks. As a reminder, for disease reduction, \(R_e\) needs to be sustained below 1.0.

Trend

Distribution of \(R_e\) Values

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Okanogan WA 14 1 1.5 647 1550 48
King WA 1 2 1.1 14446 670 196
Spokane WA 6 3 1.2 3366 680 105
Pierce WA 4 4 1.2 4868 570 107
Kitsap WA 16 5 1.3 509 190 21
Douglas WA 13 6 1.3 651 1570 26
Chelan WA 10 7 1.2 936 1240 33
Snohomish WA 3 8 1.1 5349 680 62
Benton WA 5 9 1.0 3392 1750 65
Yakima WA 2 10 0.9 10154 4070 104
Franklin WA 7 11 1.0 3206 3540 51
Grant WA 9 12 1.0 1148 1210 23
Clark WA 8 14 1.0 1702 370 34
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.1 3999 500 84
Umatilla OR 4 2 1.1 1726 2240 54
Marion OR 3 3 1.1 2423 720 44
Washington OR 2 4 1.1 2522 430 50
Deschutes OR 8 5 1.2 459 250 18
Jefferson OR 13 6 1.3 257 1110 9
Clackamas OR 5 7 1.1 1278 310 22
Lane OR 7 11 1.0 461 120 12
Malheur OR 6 14 0.8 600 1970 15
Union OR 9 23 0.6 388 1490 1
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Kern CA 6 1 1.8 16192 1830 1239
Los Angeles CA 1 2 1.1 176498 1750 3188
San Bernardino CA 4 3 1.2 29600 1390 891
Riverside CA 2 4 1.1 35180 1480 738
Fresno CA 7 5 1.2 12934 1320 406
Orange CA 3 6 1.0 35087 1110 794
San Joaquin CA 8 7 1.2 10595 1450 338
San Diego CA 5 8 1.1 27410 830 576
Alameda CA 9 16 1.1 10527 640 203

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 1.0 109942 2580 2180
Pima AZ 2 2 1.0 15061 1480 243
Yuma AZ 3 3 1.0 10297 4950 145
Pinal AZ 4 4 1.0 7543 1800 138
Mohave AZ 8 5 1.1 2790 1350 72
Gila AZ 12 6 1.3 702 1310 24
Apache AZ 6 7 1.1 2919 4080 27
Navajo AZ 5 9 0.9 5139 4730 54
Santa Cruz AZ 9 10 1.1 2513 5390 27
Coconino AZ 7 12 0.9 2882 2060 30
CO
county ST case rank severity R_e cases cases/100k daily cases
Denver CO 1 1 1.2 9118 1310 106
El Paso CO 4 2 1.1 4183 610 90
Arapahoe CO 2 3 1.1 6498 1020 70
Adams CO 3 4 1.1 5547 1120 66
Jefferson CO 5 5 1.2 3623 640 51
Chaffee CO 17 6 1.4 278 1450 17
Larimer CO 9 7 1.1 1265 370 28
Douglas CO 8 8 1.1 1524 460 30
Weld CO 6 10 1.0 3420 1160 30
Boulder CO 7 12 1.1 1785 560 19
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 18332 1640 271
Utah UT 2 2 1.1 7236 1230 152
Davis UT 3 3 1.1 2757 810 71
Washington UT 5 4 1.1 2181 1360 46
Weber UT 4 5 1.0 2380 960 60
Cache UT 6 6 1.1 1747 1430 11
Tooele UT 10 7 1.0 499 770 11
San Juan UT 8 8 1.0 588 3850 10
Summit UT 7 11 0.9 675 1670 7
Wasatch UT 9 12 0.9 517 1690 4
NM
county ST case rank severity R_e cases cases/100k daily cases
Bernalillo NM 1 1 1.1 4466 660 112
Lea NM 7 2 1.4 528 750 25
Rio Arriba NM 12 3 1.3 294 750 15
Doña Ana NM 4 4 1.1 2024 940 44
Otero NM 5 5 1.3 1037 1580 9
Valencia NM 10 6 1.2 347 460 13
McKinley NM 2 7 1.1 3905 5360 21
Santa Fe NM 8 9 1.1 505 340 13
Sandoval NM 6 10 1.1 1011 720 14
Curry NM 9 12 1.1 372 740 10
San Juan NM 3 16 0.9 2934 2300 14

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Middlesex NJ 4 1 1.2 17717 2140 41
Ocean NJ 7 2 1.2 10238 1730 29
Camden NJ 9 3 1.1 8085 1590 28
Gloucester NJ 16 4 1.1 2956 1020 16
Bergen NJ 1 5 1.0 20473 2200 26
Burlington NJ 12 6 1.1 5609 1260 16
Monmouth NJ 8 7 1.0 9943 1590 26
Essex NJ 3 8 1.0 19521 2460 19
Passaic NJ 5 10 1.0 17438 3460 16
Union NJ 6 15 0.9 16749 3030 8
Hudson NJ 2 16 0.8 19532 2920 14
PA
county ST case rank severity R_e cases cases/100k daily cases
Allegheny PA 4 1 1.0 7484 610 188
Philadelphia PA 1 2 1.1 29431 1870 146
Delaware PA 3 3 1.1 8267 1470 56
Bucks PA 5 4 1.1 6688 1070 48
Chester PA 9 5 1.1 4632 900 47
Montgomery PA 2 6 1.1 9470 1150 48
Armstrong PA 40 7 1.4 156 240 8
Berks PA 7 9 1.1 5007 1200 26
Lancaster PA 6 11 1.0 5301 980 36
Lehigh PA 8 17 1.0 4677 1290 19
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.2 10865 1310 176
Baltimore city MD 4 2 1.1 10335 1680 145
Prince George’s MD 1 3 1.1 21965 2420 137
Montgomery MD 2 4 1.0 17025 1640 95
Anne Arundel MD 5 5 1.1 6380 1120 67
Harford MD 9 6 1.2 1591 630 27
Worcester MD 16 7 1.3 499 970 16
Howard MD 6 9 1.1 3356 1060 40
Frederick MD 7 10 1.2 2886 1160 23
Charles MD 8 12 1.1 1729 1100 17
VA
county ST case rank severity R_e cases cases/100k daily cases
Virginia Beach city VA 5 1 1.3 3551 790 162
Patrick VA 64 2 1.9 91 510 6
Norfolk city VA 7 3 1.2 2837 1160 115
Newport News city VA 9 4 1.2 1491 830 65
Suffolk city VA 14 5 1.2 922 1030 32
Hampton city VA 15 6 1.2 896 660 34
Fairfax VA 1 7 1.0 15318 1340 64
Chesterfield VA 4 8 1.1 3734 1100 41
Henrico VA 6 9 1.1 3266 1000 34
Prince William VA 2 10 1.0 8464 1850 47
Loudoun VA 3 13 1.0 4840 1260 29
Arlington VA 8 26 0.9 2818 1220 13
WV
county ST case rank severity R_e cases cases/100k daily cases
Kanawha WV 2 1 1.3 686 370 26
Logan WV 23 2 1.6 77 230 5
Mingo WV 18 3 1.5 98 400 8
Ohio WV 6 4 1.2 234 550 8
Cabell WV 5 5 1.2 276 290 7
Hancock WV 20 6 1.2 90 300 5
Raleigh WV 14 7 1.2 135 180 5
Monongalia WV 1 9 0.8 892 850 23
Berkeley WV 3 11 1.0 613 540 6
Wayne WV 9 12 1.0 181 440 4
Jefferson WV 4 19 0.8 284 510 2
Wood WV 7 20 0.6 229 270 2
Randolph WV 8 22 0.6 209 720 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.1 6472 1170 54
Sussex DE 2 2 1.0 5522 2520 32
Kent DE 3 3 1.0 2124 1210 21

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Baldwin AL 7 1 1.4 2784 1340 146
Jefferson AL 1 2 1.1 10630 1610 305
Mobile AL 2 3 1.2 7313 1760 200
Calhoun AL 17 4 1.3 1118 970 56
Madison AL 4 5 1.1 4532 1270 174
Houston AL 16 6 1.3 1134 1090 47
Shelby AL 6 7 1.1 2834 1340 91
Montgomery AL 3 8 1.1 5759 2540 94
Tuscaloosa AL 5 15 1.1 3601 1750 69
Marshall AL 8 17 1.1 2732 2870 56
Lee AL 9 20 1.1 2420 1520 56
MS
county ST case rank severity R_e cases cases/100k daily cases
Jackson MS 6 1 1.5 1596 1120 88
Alcorn MS 56 2 1.7 266 720 20
Coahoma MS 35 3 1.5 556 2340 36
Hinds MS 1 4 1.3 4495 1860 138
Rankin MS 4 5 1.3 1827 1210 64
Washington MS 9 6 1.3 1306 2770 51
Harrison MS 5 7 1.2 1800 890 57
DeSoto MS 2 11 1.2 2849 1620 74
Madison MS 3 12 1.2 2047 1980 50
Jones MS 7 19 1.2 1545 2260 28
Forrest MS 8 20 1.2 1398 1850 32
LA
county ST case rank severity R_e cases cases/100k daily cases
Allen LA 26 1 1.5 984 3830 56
Calcasieu LA 6 2 1.1 5520 2760 192
East Baton Rouge LA 3 3 1.1 9738 2190 210
Vernon LA 37 4 1.4 592 1160 33
Jefferson LA 1 5 1.0 13465 3090 178
Acadia LA 15 6 1.2 2250 3600 79
St. Landry LA 17 7 1.2 1903 2280 71
Lafayette LA 4 8 1.0 5882 2450 154
Caddo LA 5 11 1.1 5649 2270 104
Tangipahoa LA 9 12 1.1 2819 2160 76
St. Tammany LA 7 13 1.1 4299 1710 90
Ouachita LA 8 18 1.1 4068 2610 76
Orleans LA 2 22 1.0 9916 2540 94

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Miami-Dade FL 1 1 1.2 106328 3920 3540
Columbia FL 30 2 1.7 2325 3360 206
Broward FL 2 3 1.1 50618 2650 1743
Wakulla FL 51 4 1.7 526 1650 47
Palm Beach FL 3 5 1.1 30714 2120 787
Bay FL 26 6 1.4 2967 1630 172
Orange FL 4 7 1.1 27611 2090 714
Duval FL 6 11 1.0 20232 2190 557
Hillsborough FL 5 12 1.0 27599 2000 612
Polk FL 9 14 1.1 11706 1750 349
Lee FL 8 17 1.0 14983 2080 376
Pinellas FL 7 21 1.0 15490 1620 318
GA
county ST case rank severity R_e cases cases/100k daily cases
Fulton GA 1 1 1.2 16028 1570 445
Cobb GA 4 2 1.2 10148 1360 270
Wayne GA 46 3 1.5 574 1930 45
Gwinnett GA 2 4 1.1 15456 1710 317
Chatham GA 6 5 1.2 4352 1520 152
DeKalb GA 3 6 1.1 11225 1510 229
Richmond GA 9 7 1.2 2837 1410 99
Clayton GA 7 8 1.1 4035 1450 98
Hall GA 5 14 1.1 4937 2520 86
Muscogee GA 8 24 1.0 3915 1990 82

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Real TX 160 1 4.9 88 2600 25
Jasper TX 120 2 2.3 178 500 17
Runnels TX 165 3 2.2 80 780 9
Bexar TX 3 4 1.2 38581 2000 1541
Harris TX 1 5 1.1 66236 1440 1611
Hidalgo TX 6 6 1.2 16271 1920 709
Cameron TX 10 7 1.3 8261 1960 394
Tarrant TX 4 10 1.1 25791 1280 610
Dallas TX 2 11 1.0 47737 1850 1028
Nueces TX 8 17 1.0 10914 3030 336
El Paso TX 7 19 1.0 13498 1610 309
Travis TX 5 24 0.9 20008 1660 326
Galveston TX 9 31 0.9 8321 2540 163
OK
county ST case rank severity R_e cases cases/100k daily cases
Jackson OK 12 1 1.8 386 1520 40
Oklahoma OK 1 2 1.1 7735 990 234
Tulsa OK 2 3 1.1 7524 1170 195
Cleveland OK 3 4 1.2 2102 760 65
Garfield OK 24 5 1.5 253 410 15
Sequoyah OK 35 6 1.5 141 340 9
Rogers OK 9 7 1.3 591 650 25
Canadian OK 5 16 1.1 838 610 28
Payne OK 8 22 1.2 597 730 10
McCurtain OK 6 23 1.1 777 2360 13
Comanche OK 7 26 1.1 670 550 13
Texas OK 4 44 0.8 1016 4810 1

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Wayne MI 1 1 1.0 26116 1480 146
Jackson MI 7 2 1.5 2294 1440 15
Macomb MI 3 3 1.1 9120 1050 80
Oakland MI 2 4 1.0 13989 1120 94
Kent MI 4 5 1.0 6785 1050 68
Genesee MI 5 6 1.1 3300 810 31
Saginaw MI 8 7 1.1 1731 900 26
Ottawa MI 9 11 1.0 1633 570 24
Washtenaw MI 6 12 1.0 2760 750 24
WI
county ST case rank severity R_e cases cases/100k daily cases
Waukesha WI 4 1 1.3 2970 740 121
Milwaukee WI 1 2 1.1 18238 1910 345
Racine WI 5 3 1.2 2913 1490 48
Kenosha WI 6 4 1.1 2272 1350 43
Marathon WI 17 5 1.2 487 360 19
Marinette WI 27 6 1.3 243 600 14
Waupaca WI 22 7 1.3 294 570 12
Outagamie WI 9 9 1.2 959 520 26
Brown WI 3 10 1.1 3779 1450 42
Walworth WI 8 12 1.1 1059 1030 23
Dane WI 2 13 0.9 3900 740 59
Rock WI 7 16 1.1 1432 890 22

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.1 16228 1310 227
Ramsey MN 2 2 1.1 6218 1150 81
Anoka MN 4 3 1.2 2984 860 48
Dakota MN 3 4 1.1 3451 830 62
Beltrami MN 31 5 1.4 156 340 11
Sherburne MN 17 6 1.3 526 560 16
Washington MN 7 7 1.1 1668 660 32
Scott MN 9 8 1.1 1171 820 24
Olmsted MN 8 13 1.0 1525 1000 18
Stearns MN 5 18 0.9 2732 1740 14
Nobles MN 6 33 0.9 1727 7910 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Minnehaha SD 1 1 1.1 3989 2140 19
Lincoln SD 4 2 1.3 465 850 7
Pennington SD 2 3 0.9 793 730 11
Davison SD 15 4 0.9 82 410 2
Beadle SD 3 5 0.9 583 3170 2
Union SD 6 6 0.8 176 1160 2
Brown SD 5 7 0.8 377 970 2
Codington SD 7 10 0.7 113 400 1
Brookings SD 8 12 0.6 112 330 1
Buffalo SD 9 13 0.5 108 5260 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Burleigh ND 2 1 1.3 755 810 30
Williams ND 5 2 1.3 221 650 14
Ward ND 6 3 1.4 147 210 8
Morton ND 4 4 1.3 223 730 9
Cass ND 1 5 1.0 2804 1610 25
Grand Forks ND 3 6 1.2 596 850 13
Stutsman ND 10 7 1.2 94 450 3
Stark ND 7 8 1.1 145 470 4
Walsh ND 9 9 0.9 100 930 4
Mountrail ND 8 10 0.8 108 1060 3

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Fairfield CT 1 1 1.2 17382 1840 40
Hartford CT 3 2 1.2 12317 1380 35
New Haven CT 2 3 1.0 12911 1500 23
Litchfield CT 4 4 1.2 1554 850 4
Tolland CT 7 5 1.1 962 640 3
New London CT 6 6 0.7 1369 510 2
Windham CT 8 7 0.7 662 570 2
Middlesex CT 5 8 0.7 1369 840 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Middlesex MA 1 1 1.1 25312 1590 62
Worcester MA 4 2 1.1 13072 1590 34
Norfolk MA 5 3 1.1 9965 1430 39
Suffolk MA 2 4 1.0 20876 2640 43
Essex MA 3 5 1.0 16956 2170 35
Bristol MA 7 6 1.0 8880 1590 29
Hampden MA 8 7 1.0 7275 1550 21
Barnstable MA 9 8 1.2 1689 790 10
Plymouth MA 6 9 1.1 8977 1750 13
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.0 14112 2220 60
Kent RI 2 2 0.9 1365 830 7
Washington RI 3 3 0.8 579 460 2
Bristol RI 5 4 0.7 295 600 2
Newport RI 4 5 0.5 372 450 1

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1.0 228511 2710 349
Suffolk NY 3 2 1.0 42885 2880 59
Nassau NY 2 3 1.0 42957 3170 46
Erie NY 7 4 1.0 8313 900 41
Westchester NY 4 5 1.0 35771 3690 35
Albany NY 11 6 1.1 2448 800 18
Monroe NY 8 7 1.0 4607 620 30
Orange NY 6 11 1.0 11019 2910 11
Dutchess NY 9 14 0.9 4429 1510 9
Rockland NY 5 16 0.9 13849 4280 9

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Chittenden VT 1 1 0.9 716 440 4
Bennington VT 5 2 0.6 83 230 1
Rutland VT 4 3 0.6 84 140 0
Franklin VT 2 4 0.6 114 230 0
Windham VT 3 5 0.4 102 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Cumberland ME 1 1 0.9 2015 690 10
Androscoggin ME 3 2 1.1 529 490 2
York ME 2 3 0.9 618 300 3
Kennebec ME 4 4 0.9 157 130 1
Penobscot ME 5 5 0.7 141 90 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.1 3658 890 20
Rockingham NH 2 2 0.9 1583 520 4
Merrimack NH 3 3 0.8 452 300 2
Strafford NH 4 4 0.9 316 250 1
Carroll NH 8 5 0.8 80 170 1
Belknap NH 5 6 0.6 102 170 1
Grafton NH 6 7 0.5 102 110 0
Cheshire NH 7 8 0.3 82 110 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 4 1 1.1 7192 1760 178
Florence SC 11 2 1.2 2488 1800 77
Hampton SC 43 3 1.5 263 1330 17
Lexington SC 5 4 1.1 4269 1490 107
Cherokee SC 31 5 1.4 472 830 23
Greenville SC 2 6 1.0 9541 1910 177
Charleston SC 1 7 1.0 10919 2770 225
York SC 9 9 1.1 2885 1120 82
Beaufort SC 8 10 1.1 3007 1650 84
Berkeley SC 6 12 1.0 3577 1710 101
Horry SC 3 16 0.9 7595 2370 123
Spartanburg SC 7 18 1.0 3540 1170 78
NC
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg NC 1 1 1.0 19462 1850 332
Wake NC 2 2 1.0 10159 970 204
Pitt NC 21 3 1.3 1461 820 47
Chowan NC 83 4 1.6 95 670 7
Cumberland NC 9 5 1.2 2325 700 62
Buncombe NC 22 6 1.2 1454 570 52
Guilford NC 4 7 1.1 4749 910 92
Gaston NC 6 10 1.1 2735 1260 74
Durham NC 3 12 1.0 5562 1810 73
Forsyth NC 5 13 1.0 4605 1240 68
Union NC 8 16 1.0 2532 1120 54
Johnston NC 7 28 1.0 2658 1390 43

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Big Horn MT 3 1 1.6 214 1600 12
Flathead MT 5 2 1.5 192 200 13
Gallatin MT 2 3 1.2 803 770 33
Cascade MT 7 4 1.4 112 140 7
Yellowstone MT 1 5 1.0 910 580 34
Missoula MT 4 6 1.2 210 180 7
Lake MT 6 7 1.1 150 500 8
Lewis and Clark MT 8 8 1.1 110 160 5
WY
county ST case rank severity R_e cases cases/100k daily cases
Teton WY 3 1 1.4 282 1220 12
Lincoln WY 9 2 1.4 88 460 5
Sweetwater WY 5 3 1.1 223 510 7
Laramie WY 2 4 1.0 421 430 6
Fremont WY 1 5 1.0 455 1140 5
Albany WY 10 6 1.1 82 220 3
Park WY 8 7 1.0 100 340 2
Natrona WY 6 8 0.9 194 240 2
Uinta WY 4 9 0.8 236 1150 2
Campbell WY 7 10 0.8 108 230 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Canyon ID 2 1 1.2 4292 2020 187
Ada ID 1 2 1.1 7358 1650 244
Bonneville ID 6 3 1.5 516 460 32
Kootenai ID 3 4 1.1 1366 890 52
Jefferson ID 21 5 1.4 91 330 7
Bannock ID 10 6 1.2 308 360 12
Minidoka ID 9 7 1.2 372 1800 9
Twin Falls ID 4 9 1.0 1041 1240 16
Cassia ID 7 11 1.0 415 1760 7
Jerome ID 8 19 0.7 381 1630 4
Blaine ID 5 21 0.6 564 2560 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Hancock OH 44 1 1.8 216 290 17
Franklin OH 1 2 1.1 15903 1250 297
Lucas OH 4 3 1.2 4081 940 94
Cuyahoga OH 2 4 1.0 11844 940 197
Montgomery OH 5 5 1.1 3528 660 82
Licking OH 20 6 1.3 918 530 31
Ross OH 43 7 1.5 258 330 12
Hamilton OH 3 10 1.0 8598 1060 117
Summit OH 6 13 1.1 2906 540 45
Butler OH 8 15 1.1 2424 640 46
Marion OH 7 43 1.1 2824 4320 6
Pickaway OH 9 49 0.9 2314 4030 6
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.1 102355 1960 536
Saline IL 50 2 1.6 91 380 9
DuPage IL 3 3 1.1 10861 1170 94
St. Clair IL 6 4 1.1 3516 1330 82
Madison IL 9 5 1.2 1815 680 53
Lake IL 2 6 1.1 11411 1620 83
Jackson IL 18 7 1.4 496 850 15
Will IL 5 8 1.1 8094 1180 63
Kane IL 4 13 1.1 8754 1650 46
McHenry IL 8 16 1.1 2716 880 31
Winnebago IL 7 17 1.1 3503 1220 25
IN
county ST case rank severity R_e cases cases/100k daily cases
Marion IN 1 1 1.2 13754 1460 131
Vanderburgh IN 10 2 1.2 1482 820 62
Hamilton IN 6 3 1.2 2236 710 44
Lake IN 2 4 1.1 6733 1380 82
Dubois IN 28 5 1.3 551 1300 22
St. Joseph IN 5 6 1.1 2820 1050 52
Monroe IN 23 7 1.3 600 410 23
Elkhart IN 3 8 1.0 4455 2190 57
Allen IN 4 15 1.1 3365 910 31
Johnson IN 9 20 1.1 1575 1040 16
Hendricks IN 8 25 1.1 1663 1030 14
Cass IN 7 26 1.2 1711 4490 6

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Henderson TN 39 1 1.7 324 1160 30
Knox TN 5 2 1.4 3225 710 148
Washington TN 23 3 1.5 674 530 45
Shelby TN 2 4 1.1 18915 2020 390
Roane TN 53 5 1.7 221 420 16
Davidson TN 1 6 1.0 20081 2940 417
Blount TN 18 7 1.5 815 630 50
Rutherford TN 3 10 1.2 5418 1760 147
Hamilton TN 4 12 1.1 5046 1410 128
Sumner TN 7 21 1.1 2944 1640 77
Williamson TN 6 23 1.0 3014 1380 89
Wilson TN 8 28 1.1 1895 1430 52
Trousdale TN 9 76 0.9 1565 16350 4
KY
county ST case rank severity R_e cases cases/100k daily cases
Harlan KY 33 1 1.8 172 630 18
Oldham KY 9 2 1.6 532 810 41
Jefferson KY 1 3 1.3 6071 790 142
Scott KY 24 4 1.6 246 460 14
Boyle KY 50 5 1.6 97 320 6
Barren KY 22 6 1.4 255 580 14
Fayette KY 2 7 1.1 2812 880 64
Warren KY 3 8 1.2 2215 1750 39
Kenton KY 4 9 1.2 1195 730 27
Boone KY 5 18 1.1 937 730 17
Daviess KY 7 21 1.1 638 640 10
Shelby KY 6 29 1.0 666 1420 8
Muhlenberg KY 8 48 0.9 620 1990 4

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Nodaway MO 49 1 2.3 80 350 9
St. Louis MO 1 2 1.3 10821 1080 279
Camden MO 33 3 1.8 184 410 13
Polk MO 34 4 1.6 182 580 19
St. Charles MO 3 5 1.2 3095 790 122
St. Louis city MO 2 6 1.2 3971 1280 88
Greene MO 7 7 1.3 1073 370 49
Jackson MO 4 8 1.2 2690 390 81
Jefferson MO 5 11 1.2 1179 530 40
Jasper MO 6 17 1.1 1098 920 27
Boone MO 8 21 1.0 1069 610 26
Buchanan MO 9 37 1.0 1020 1150 6
AR
county ST case rank severity R_e cases cases/100k daily cases
Newton AR 54 1 4.6 81 1030 21
Independence AR 31 2 1.8 194 520 15
Garland AR 17 3 1.4 662 670 33
Pulaski AR 2 4 1.1 4405 1120 116
Craighead AR 13 5 1.3 958 910 38
Franklin AR 55 6 1.6 76 430 6
Washington AR 1 7 1.1 5697 2490 85
Sebastian AR 5 8 1.1 1425 1120 47
Benton AR 3 10 1.0 4232 1630 59
Crittenden AR 8 13 1.2 1085 2210 21
Jefferson AR 6 15 1.1 1176 1670 26
Faulkner AR 9 21 1.1 1069 870 23
Lincoln AR 7 41 0.9 1157 8450 7
Hot Spring AR 4 50 0.4 1463 4360 12

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 709.7 seconds to compute.
2020-07-27 13:50:17

version history

Today is 2020-07-27.
68 days ago: Multiple states.
60 days ago: \(R_e\) computation.
57 days ago: created color coding for \(R_e\) plots.
52 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
52 days ago: “persistence” time evolution.
45 days ago: “In control” mapping.
45 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
37 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
32 days ago: Added Per Capita US Map.
30 days ago: Deprecated national map.
26 days ago: added state “Hot 10” analysis.
21 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
19 days ago: added per capita disease and mortaility to state-level analysis.
7 days ago: changed to county boundarieson national map for per capita disease.
2 days ago: corrected factor of two error in death trend data.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.